10 Must-Know Machine Learning Algorithms for 2024

Machine learning algorithms play a pivotal role in the field of artificial intelligence and are revolutionizing various industries. By 2030, the machine-learning market can grow from USD 26.03 billion in 2023 to USD 225.91 billion . As we step ahead, it becomes increasingly important to understand the most common machine-learning algorithms that power intelligent systems and drive data-driven decision-making.??

By the end, you will have a solid understanding of top machine learning algorithms, enabling you to navigate the evolving landscape of machine learning.?

List of 10 Popular Machine Learning Algorithms?

1.Linear regression?

Linear regression is widely used among the best 10 machine learning algorithms that aim to predict the value of a dependent variable based on one or more independent variables. The algorithm constructs a linear equation that best fits the given data, minimizing the sum of squared errors between the predicted values and the actual ones.??

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2.Logistic regression?

Logistic regression is a widely used machine learning algorithm for binary classification tasks. It predicts the probability of an event occurring by modeling the relationship between a set of input variables and a binary output variable. Unlike linear regression, logistic regression uses the logistic function to produce a value between 0 and 1, which represents the probability of the event.??

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3.Naive Bayes?

This is a popular machine learning algorithm based on the Bayes theorem, which assumes that all features are independent of each other. It is often used for classification tasks, where the algorithm predicts the probability of a given example belonging to a particular class. Naive Bayes works by calculating the conditional probabilities of each feature given the class and then uses these probabilities to calculate the probability of the class given the features.??

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4.Decision tree?

The decision tree machine learning algorithm is next in the common machine learning algorithms list and is a predictive modeling technique that creates a flowchart-like structure or tree to make decisions. It uses a combination of if-else conditions to classify or predict an outcome based on a set of input features. Each node in the tree represents a decision point based on a feature, and the edges represent the possible outcomes or paths to take. The algorithm learns from a training dataset by recursively partitioning the data based on different attributes to maximize information gain or minimize impurity.??

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5.Random Forest?

This is a popular algorithm in machine learning that combines the power of multiple decision trees to make accurate predictions. It uses a technique called ensemble learning, where a group of individual models, known as decision trees, are created and their outputs are aggregated to reach a final prediction. Random forest introduces randomness by randomly selecting a subset of features and data samples for each decision tree, reducing overfitting, and improving generalization.??

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6.K-nearest neighbor (KNN)?

The k-nearest neighbor (k-NN) algorithm is a popular and simple machine learning model used for classification and regression tasks. It operates on the principle of similarity, where an unlabeled sample is classified based on its similarity to other labeled samples in the training dataset. In this algorithm, k represents the number of nearest neighbors that are considered for the classification. The distance metric, such as Euclidean distance, is used to measure the similarity between the unlabeled sample and the labeled samples.??

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7.K-means?

K-means is a popular unsupervised machine-learning algorithm used to cluster data points into distinct groups or categories. It aims to minimize the intra-cluster variance, ensuring that similar data points are grouped. The algorithm starts by randomly selecting the k number of centroids, which act as the center of the clusters. It then assigns each data point to the nearest centroid, creating initial clusters. The centroids are then recalculated based on the meaning of data points in each cluster.??

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8. Support vector machine (SVM)?

Support Vector Machine (SVM) is a powerful and widely used machine learning algorithm that falls under the category of supervised learning. It is primarily used for classification tasks, although it can also be applied to regression problems. SVM separates data points into different classes by constructing a hyperplane in a high-dimensional space. The algorithm aims to find the optimal hyperplane that maximizes the margin between the data points of different classes, thus improving the model's ability to generalize well to unseen data.??

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9.Apriori?

The Apriori machine learning algorithm is a popular and widely used approach in data mining for discovering association rules from large datasets. It is a bottom-up, iterative method that identifies frequent item sets and uses them to generate rules. The algorithm works by constructing a set of candidate item sets by combining smaller item sets and then scans the dataset to determine which candidates meet the minimum support threshold.??

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10.Gradient boosting?

Gradient boosting is a powerful ML algorithm that combines multiple weak predictive models, often decision trees, to create a strong and accurate model. It is an iterative process that aims to minimize the errors made by the previous models by tweaking subsequent models to focus on these errors. This algorithm makes use of a gradient descent optimization technique where it continuously adjusts model parameters in the direction of the negative gradient of the loss function.??

Conclusion?

?We hope this article provides you with an overview of essential ML algorithms that you must understand. These top machine learning algorithms have revolutionized the field by enabling the development of highly accurate models that can learn from data and make predictions.??

With the help of skilled AI and ML developers from JumpGrowth, you can automate business processes and create apps with potent algorithms that can learn, develop, forecast, and expand.

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